Significantly improve the speed and quality of Radiology reporting by reducing unnecessary dictation, particularly for ultrasound and DEXA. Imorgon transfers modality measurements into Powerscribe/Fluency/RadAI merge fields/tokens, eliminating manual entry errors.
Imorgon's specialized services offer the following advantages:
- All measurements are always transferred (usually DICOM SR)
- Electronic worksheets capture findings and insert them into Powerscribe/Fluency/RadAI (rather than dictating from a worksheet)
- Worksheets with priors, calculators, and clinical decision support (TI-RADS, O-RADS, etc)
- Integrate into Epic or other EHRs
- Vendor neutral
- Support to ensure everything continues working
Significant improvement in the overhead of reporting with a quick ROI.
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LM-Kit.NET serves as a comprehensive toolkit tailored for the seamless incorporation of generative AI into .NET applications, fully compatible with Windows, Linux, and macOS systems. This versatile platform empowers your C# and VB.NET projects, facilitating the development and management of dynamic AI agents with ease.
Utilize efficient Small Language Models for on-device inference, which effectively lowers computational demands, minimizes latency, and enhances security by processing information locally. Discover the advantages of Retrieval-Augmented Generation (RAG) that improve both accuracy and relevance, while sophisticated AI agents streamline complex tasks and expedite the development process.
With native SDKs that guarantee smooth integration and optimal performance across various platforms, LM-Kit.NET also offers extensive support for custom AI agent creation and multi-agent orchestration. This toolkit simplifies the stages of prototyping, deployment, and scaling, enabling you to create intelligent, rapid, and secure solutions that are relied upon by industry professionals globally, fostering innovation and efficiency in every project.
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GigaChat
GigaChat excels in responding to user inquiries, engaging in interactive conversations, generating programming code, and crafting written content and images based on user-provided descriptions, all within a unified framework. Unlike other neural networks, GigaChat is intentionally built to support multimodal interactions and showcases exceptional skill in the Russian language.
At its core, GigaChat is based on the NeONKA (NEural Omnimodal Network with Knowledge-Awareness) model, which integrates a wide range of neural network systems and utilizes methods like supervised fine-tuning and reinforcement learning that is augmented by human feedback. Consequently, Sber's pioneering neural network can effectively address a multitude of cognitive tasks, including engaging in stimulating dialogues, creating informative written content, and providing accurate answers to questions. Additionally, the incorporation of the Kandinsky 2.1 model within this framework significantly boosts its abilities, allowing it to generate detailed images in response to user prompts, which broadens the possible uses of the service. This diverse functionality not only enhances GigaChat’s versatility but also positions it as a leading tool in the field of artificial intelligence, making it a valuable asset for various applications.
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DeepSeek-V2
DeepSeek-V2 represents an advanced Mixture-of-Experts (MoE) language model created by DeepSeek-AI, recognized for its economical training and superior inference efficiency. This model features a staggering 236 billion parameters, engaging only 21 billion for each token, and can manage a context length stretching up to 128K tokens. It employs sophisticated architectures like Multi-head Latent Attention (MLA) to enhance inference by reducing the Key-Value (KV) cache and utilizes DeepSeekMoE for cost-effective training through sparse computations. When compared to its earlier version, DeepSeek 67B, this model exhibits substantial advancements, boasting a 42.5% decrease in training costs, a 93.3% reduction in KV cache size, and a remarkable 5.76-fold increase in generation speed. With training based on an extensive dataset of 8.1 trillion tokens, DeepSeek-V2 showcases outstanding proficiency in language understanding, programming, and reasoning tasks, thereby establishing itself as a premier open-source model in the current landscape. Its groundbreaking methodology not only enhances performance but also sets unprecedented standards in the realm of artificial intelligence, inspiring future innovations in the field.
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